SDAProfilePhi

the output of <code><a rd-options="" href="/link/SDALGCPMCML?package=SDALGCP&version=0.4.0" data-mini-rdoc="SDALGCP::SDALGCPMCML">SDALGCPMCML</a></code> of class "SDALGCP"

This function plots the profile likelihood of phi

Provides a computationally efficient discrete approximation to log-Gaussian Cox process model for spatially aggregated disease count data. It uses Monte Carlo Maximum Likelihood for model parameter estimation as proposed by Christensen (2004) <doi: 10.1198/106186004X2525> and delivers prediction of spatially discrete and continuous relative risk. It performs inference for static spatial and spatio-temporal dataset. The details of the methods are provided in Johnson et al (2019) <doi:10.1002/sim.8339>.

Olatunji Johnson

SDALGCP

Spatially Discrete Approximation to Log-Gaussian Cox Processes
for Aggregated Disease Count Data

Emanuele Giorgi

Peter Diggle

SDAProfilePhi function

the output of <code><a rd-options='' href='SDALGCPMCML'>SDALGCPMCML</a></code> of class "SDALGCP"

SDAProfilePhi: plot profile likelihood of phi

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Details